Monitoring and improving operating efficiencies such as timetable optimization, energy consumption, and other operating variables during train operation is ongoing in the railway industry.
U.S. application Ser. No. 14/168,645 discloses methods and systems for optimizing energy consumption along a train line by synchronizing two more vehicles on the train line. Timetables for a train line are used and optimized based on aggregated information/data. The data may include, for example, dwell time, departure time, arrival time, train patterns/trip linking, energy profiles, origin/destination matrix, speed profile, and is standardized against a reference level and variations in driver behaviour accounted for. Timetables of trains in real time are adjusted based on the collected data.
U.S. Pat. No. 8,751,073, and U.S. Pat. No. 8,370,007 describe a trip optimization system that involves determining a driving strategy of a train. A trip plan is created using input information including, train position, consist composition, locomotive tractive power performance of locomotive traction transmission, consumption of engine fuel as a function of output power, cooling characteristics, intended trip route, car makeup and loading, desired trip parameters, and a trip profile is computed. A quantitative objective function, comprising a weighted sum of model variables that correspond to a fuel consumption rate and emissions generated plus a term to penalize excess throttle variations, is used to calculate the trip profile. Real-time train data is used to estimate locomotive and/or train parameters, and the estimated parameters are compared to the input information. Any differences between the estimated parameters and input information are used to modify the trip plan in real time. Additionally, a train parameter may be adjusted based on the data collected.
U.S. Pat. No. 6,748,303 teaches a method of analyzing train operational data collected onboard a train. Operational and informational parameters are derived from the operational data for each run and the operational parameters are compared to selected exception values. The operational parameters may include fuel consumption, in-train forces, time to destination, speed limit adherence, air brake preference and dynamic brake preference. A train performance analysis (TPA) report is created and a target run (i.e. an optimal performance prediction of the run under analysis) may be created.
U.S. Pat. No. 7,188,341 discloses a method of adjusting a simulator and processing data from an event recorder of a train. The parameters that are adjusted include grade resistance, curve resistance, rolling resistance, tractive effect of trains, locomotives, dynamic brake effect of the train's locomotives, pneumatic brake system and train weight. The input data is analyzed on the simulator, and any anomalies that are identified, along with the information about the train (location, time, crew, train makeup, railroad, log file numbers) is used to for further analysis and/or real time adjustment. U.S. Pat. No. 7,647,141 also teaches the use of a simulator in real time.
U.S. Pat. No. 6,332,106 teaches a method for analyzing train handling by setting a standard for a train run, and then collecting train operating and handling data from the actual run, determining operating constraints during the run which are not included in the standard run, and determining the differences between the operating constraints during the run to those included in the standard run. The train handling data is compared to the standard and the comparison is adjusted for the operating constraints and used to penalize driver behaviour. Operating constraints include, for e.g., speed limits, special slow orders, speed restriction zones, meets and passes, track occupancy permits, general operating bulletins, upcoming signal designations, desired time of arrival, final destination location, limit of authority or track locations to which train may move without possibility of interfering with the movement of the trains in the area)
Operation efficiencies of a train may also be varied by modifying one or more operating parameters during a train run. Example of operating parameters that may be modified may include, but are not limited to, the type of fuel used to operate the train, type and location of aerodynamic faring, use of a wind skirt, acceleration, deceleration of the train over a railroad track, altering time-in-notch patterns, application of one or more friction control composition, location of application of the friction control composition, and the like.
In order to assess the value of using or modifying a particular operating parameter, data on train runs and a train's performance may be collected and used to analyze the impact of using or modifying a particular operating parameter. This data is typically collected during regular train runs (i.e. field conditions) as opposed to data collected in a controlled environment such as a test track. Determining the actual impact of these modifications on a train's performance under field conditions with confidence, including, without limitation, fuel consumption, energy consumption or both fuel consumption and energy consumption, however, is challenging. Although a significant amount of data can be collected during a train run, the analysis of this data to quantify the impact of various operating parameters on train performance under field conditions may be limited due to the variability in the data between different train runs. The variability in the data may arise from a number of different sources, including environmental factors, such as wind, rain, snow, and other factors, such as driver behaviour.